🤖 AI Summary
This study addresses the challenge psychotherapists face in acquiring Acceptance and Commitment Therapy (ACT) skills due to the lack of safe, standardized training environments. To bridge this gap, we propose the first fidelity-aware AI virtual patient system, which leverages large language models (e.g., GPT-4o-mini) to generate naturalistic, therapy-record-informed dialogues and integrates an independent evaluation module that delivers real-time, turn-by-turn feedback aligned with established ACT fidelity criteria. The system enables highly realistic, configurable deliberate practice, significantly enhancing therapists’ awareness and willingness to employ ACT interventions. Expert evaluations confirm the virtual patient’s behavioral realism, and experiments on 49 therapy transcripts demonstrate strong agreement between model-generated fidelity scores and those of human supervisors (MAE = 6.12, p < 0.01).
📝 Abstract
Training psychotherapists in evidence-based interventions such as Acceptance and Commitment Therapy (ACT) requires repeated practice with meaningful feedback, yet opportunities for safe, standardized training are limited by ethical, logistical, and resource constraints. We introduce a system designed to support ACT-oriented psychotherapy training through spoken dialogue with an embodied virtual patient. The system uses large language models to simulate patient behavior conditioned on profiles derived from real therapy sessions and configurable clinical scenarios, while a separate automated evaluator provides turn-by-turn feedback on therapist responses based on established ACT fidelity criteria. Rather than aiming to replace supervision, the system is intended to support deliberate practice by enabling experimentation, reflection, and immediate feedback in low-risk settings. Expert evaluation with practicing psychologists confirmed high realism in patient behavior and demonstrated that immediate turn-by-turn ACT feedback increased therapists' awareness of intervention choices and enabled effective experimentation with alternative responses. Quantitative evaluation across 49 therapy transcripts identified GPT-4o-mini as the optimal feedback model, achieving the lowest mean absolute error (MAE = 6.12) in replicating human supervisor ACT fidelity ratings with statistically significant agreement. This work demonstrates the potential of fidelity-aware simulated patients as a scalable complement to psychotherapy training.